Smart Machines and Marketing: What’s Next?

In addition to being a quasi-mystical gladiatorial wonder of early ’60s cinema, Quo Vadis? is a Latin phrase meaning: “Where are you going?” And as though bumping into St. Peter on his way out of Rome, we can’t help but ask our sentient silicon brothers: What’s next for marketers?

First, let’s admit the machines have poked through the perimeter and are marching rather randomly through the halls. First came the human advance guard with their custom code to help us do lead scoring and clustering for some (rather hard-to-understand) segments. Instead of “Urban Moms 25-34” we were given “Cluster Zy7” or something. Okay.

Then the products came: the recommender systems for ecommerce and media content (“You might also like …”); the propensity and brand affinity and likelihood-to-churn predictors (“One more email and this guy will disappear”); and the smart search tools. Of course, much of this “intelligence” was very narrow: cleaner email lists and better-converting sweater widgets on our commerce storefront. But it was a start.

What’s Next?

Marketing is a practical domain. It is focused on results and rewarded for rapid success. Its time frames are very short and its methods eccentric. On the plus side, marketers are natural experimenters with good failure tolerance. They’ll try almost anything once.

Trying to predict where algorithmic marketing is going, we’ll assume whatever happens serves the CMO’s agenda. In other words, the machines will be pointed at problems the CMO wants to solve. Flying cars would be a sure thing if the CMO’s bonus were somehow tied to her altitude. But it isn’t, so they aren’t.

As far as I can tell, the CMO has to do three things:

Understand customers and markets

Create strategies and assets

Manage programs, products and people

Understanding People and Markets

Machine learning is one activity of smart machines. It can be defined as a set of algorithms that learn from experience. These algorithms are generally used either to make predictions or to find structure in data. Most of the machine learning applied to marketing so far has been in the first area: understanding customers.

Today: We’ve seen segmentation (or clustering), and various classification models that determine how likely a person is to belong to a group that has a useful label like “Likely to buy Nike” or “Likely to be high value” or even just “Likely to buy something.” Platforms like Adobe Analytics and SAS offer clustering: RichRelevance and AgilOne offer predictive models. There are many others.

Tomorrow:Expect predictive models to be get better, faster and more specific. Today, these models use a limited set of attributes to make predictions, mostly a few demographic and some behavioral attributes. For example, the machine knows your gender and age and income, as well as some things it’s tracked you doing (opened an email, visited the Bernese mountain dog cufflink section of the site), and it goes from there. In future, context will become more important: not just location and time of day and the weather, but what’s surrounding you (physical environment), who you’re with (social environment), and your vital signs (personal environment).

What else? The psychological dimension: what you’re thinking. People have different tendencies, biases, perceptions, opinions. Incorporating mental data would make predictions much better. Today the only way to get at this is to ask (survey), listen (text analytics), or make crude inferences based on behaviors (probably happy around dogs). In the future, machines will be much better at accurate text analytics and at inferring psychology from our actions. A pre-school version of this is Facebook’s “dwell time” measure that tracks when you’re showing unexpected interest.

In addition to understanding people, marketers must understand markets. Machines already aid customer research using sensors and in future they should be able to take advantage of a range of text, image and sensor data to recommend strategic decisions: new product ideas and new markets to enter.

Create

Machines are not people, and smart machines are not smart people. Of the three marketing responsibilities, the last to fall to the robo-pincers will be this second one: creativity. It is intimately human.

Today:One of Gartner’s Top 10 Predicts at last fall’s Symposium/ITExpo was that “By 2018, 20% of all business content will be authored by machines.” Robo-writers already exist. One of our Cool Vendors for Data-Driven Marketing this year was a platform called Persado, which creates auto-generated text for email, websites, and other marketing purposes. It uses a fancy taxonomy to get at the emotional valence of words and uses that information to improve messages. There are others in this space, but their application so far is narrow.

Tomorrow: Robo-writers should improve to the point where anything short of poetry could be written by machines. And they’ll enter the world of visual communication, creating and testing imagery and designs. After that, we should expect machine-created video that may incorporate human ideas and templates, weaving them into countless versions that include elements we would call creative.

Manage

You can’t manage what you can’t measure, and marketing and advertising measurement continues to progress. There is also the important business of forecasting demand, optimizing prices and product mix, competitive planning and loyalty programs. Not to mention the non-stop agenda of improving the impact of your own channels: the website, mobile apps, email, and so on.

Today:We have seen the evolution of early tools like A/B testing into multivariate and then multisegment/multivariate and now cross-channel personalization. And the development of sophisticated attribution and marketing mix models. Like all evolution, it is too slow and not pretty enough. But the goal is obvious. Marketers want to collect user-level information across channels and devices; as much information as they can about that person; and determine what combination of time, format and message will get them to convert or improve their LTV. Many solutions exist here.

Tomorrow:As my colleague Andrew Frank has argued and will continue to argue at our Gartner Digital Marketing Conference in San Diego in May (more information here), we expect the future will see the convergence of all of marketing’s “Four P’s” (product, price, promotion, place). Rather than post hoc measurement leading to “optimization” and then more measurement, our machines will be able to capture a contextual picture of each individual customer, including their history. Instead of robo-writing the email most likely to lead to a sale, the machine will determine:

Promotion: machine-generated messages diabolically calculated to persuade you based on both rational and irrational biases (afraid of the dark? brightly-lit images for you!)

Place: everywhere is a store!

We’ll see where we go. We’ll know where we are when we get there. But one thing’s certain. We won’t be alone.

Comments are closed

3 Comments

Anna Winskillsays:

Continuing to enjoy your writing and humor, Marty! How are those Bernese mountain dog cufflinks working out?! Wondering if you heard about Microsoft’s chatbot “Tay” getting miseducated by Twitter? Any specific thoughts on what intersection of marketing + chatbots might look like tomorrow?

I love the sentence that we help to understand people, while marketers must understand markets. Neuralya (www.neuralya.com) is a powerful tool for customer research using sensors and it covers the prototyping and validation phase, since it works in a controlled environment. I do think that a real step change in supporting strategic decisions can be made with real-time data collection in a live environment, when marketers will be able to verify the effectiveness of their choices. Extending a Real-Time Store Monitoring Platform with new generation sensors can help to have a deeper understanding of customers’ purchase behaviour.